Bibliographic record
Abstract
Software projects are known for their failure rate, where many are being delivered late, over budget or being canceled while in development. The reason to it is changing requirements and intangibility of the software. Being so abstract it is difficult to imaging all the aspects of the software at the requirements stage. Also technology is playing a major role since processing power, storage space, and data transfer speeds are improving from year to year. Agile methodologies are addressing projects with unclear requirements making process of implementing new specifications along the project much easier and less costly. However the success rate of the software projects did not improve much since the introduction of Agile methodologies. This thesis is looking at what type of projects fit different methodologies and what are factors which practitioners should take into account when selecting methodology for a particular project, The thesis opens up with introduction which sets the research question and provides a brief background to the research topic. In subsequent chapter literature review is conducted to find out what does literature and other researchers have said on the same topic. Third chapter discusses underlying research philosophy and discusses the data collection tools. Next chapter discusses the findings of the research. Interviews has been conducted with project management professionals from Sweden, US, UK and Canada. It was identified through the analysis of patters that Agile methodologies are not well suited for projects involving databases, embedded development and computationally complex projects. Through the analysis of the questionnaire several project characteristics were identified which suit Agile methodologies better than traditional ones: unclear requirements, high risk of failure etc… In the last chapter the thesis concludes the findings and its theoretical and practical implications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".